Suchergebnisse

Abstract
Background
There are studies that analyze the role of meteorological variables on the incidence and severity of COVID-19, and others that explore the role played by air pollutants, but currently there are very few studies that analyze the impact of both effects together. This is the aim of the current study. We analyzed data corresponding to the period from February 1 to May 31, 2020 for the City of Madrid. As meteorological variables, maximum daily temperature (Tmax) in ºC and mean daily absolute humidity (AH) in g/m3 were used corresponding to the mean values recorded by all Spanish Meteorological Agency (AEMET) observatories in the Madrid region. Atmospheric pollutant data for PM10 and NO2 in µg/m3 for the Madrid region were provided by the Spanish Environmental Ministry (MITECO). Daily incidence, daily hospital admissions per 100.000 inhabitants, daily ICU admissions and daily death rates per million inhabitants were used as dependent variables. These data were provided by the ISCIII Spanish National Epidemiology Center. Generalized linear models with Poisson link were performed between the dependent and independent variables, controlling for seasonality, trend and the autoregressive nature of the series.

Results
The results of the single-variable models showed a negative association between Tmax and all of the dependent variables considered, except in the case of deaths, in which lower temperatures were associated with higher rates. AH also showed the same behavior with the COVID-19 variables analyzed and with the lags, similar to those obtained with Tmax. In terms of atmospheric pollutants PM10 and NO2, both showed a positive association with the dependent variables. Only PM10 was associated with the death rate. Associations were established between lags 12 and 21 for PM10 and between 0 and 28 for NO2, indicating a short-term association of NO2 with the disease. In the two-variable models, the role of NO2 was predominant compared to PM10.

Conclusions
The results of this study indicate that the environmental variables analyzed are related to the incidence and severity of COVID-19 in the Community of Madrid. In general, low temperatures and low humidity in the atmosphere affect the spread of the virus. Air pollution, especially NO2, is associated with a higher incidence and severity of the disease. The impact that these environmental factors are small (in terms of relative risk) and by themselves cannot explain the behavior of the incidence and severity of COVID-19.

Abstract
Background
While many studies analyze the effect of extreme thermal events on health, little has been written about the effects of extreme cold on mortality. This scarcity of papers is particularly relevant when we search studies about extreme cold on the health of rural population. Therefore, we tried to analyze the effect of cold waves on urban areas and rural areas from Madrid and to test whether differentiated effects exist between both population classes. For this purpose, we analyzed data from the municipalities with over 10,000 inhabitants for the period from January 1, 2000 through December 31, 2013. Municipalities were classified as urban or rural (Eurostat), and they were grouped into similar climatological zones: Urban Metropolitan Centre (UMC), Rural Northern Mountains (RNM), Rural Centre (RC) and Southern Rural (SR). The dependent variable was the daily mortality rate due to natural causes per million inhabitants (CIE-X: A00-R99) that occurred between the months of November and March for the period. The independent variable was minimum daily temperature (ºC) (Tmin). Social and demographic contextual variables were used, including: population > age 64 (%), deprivation index and housing indicators. The analysis was carried out in three phases: (1) determination of the threshold temperature (Tthreshold) which defines the cold waves; (2) determination of the relative risk (RR) for cold waves using Poisson linear regression (GLM); and (3) using GLM of the binomial family, Odds Ratios (OR) were calculated to analyze the relationship between the frequency of the appearance of cold waves and the socioeconomic variables.

Results
The UMC zone experienced 585 extreme cold events related to attributable increases in the mortality rate. The average number of cold waves in the rural zones was 319. The primary risk factor was the percentage of population over age 64, and the primary protective factor was housing rehabilitation. As a whole, the period experienced more cold waves (1542) than heat waves (1130).

Conclusion
The UMC was more vulnerable than the rural areas. Furthermore, the results support the development of prevention policies, especially considering the fact that cold wave events were more frequent than heat waves.

Abstract
Background
The objective of this study was to identify which air pollutants, atmospheric variables and health determinants could influence COVID-19 mortality in Spain. This study used information from 41 of the 52 provinces in Spain (from Feb. 1, to May 31, 2021). Generalized Linear Models (GLM) with Poisson link were carried out for the provinces, using the Rate of Mortality due to COVID-19 (CM) per 1,000,000 inhabitants as dependent variables, and average daily concentrations of PM10 and NO2 as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). The GLM model controlled for trend, seasonalities and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 g/m3 in PM10 and NO2 and by 1 ℃ in the case of Tmax and 1 g/m3 in the case of HA. Later, a linear regression was carried out that included the social determinants of health.

Results
Statistically significant associations were found between PM10, NO2 and the CM. These associations had a positive value. In the case of temperature and humidity, the associations had a negative value. PM10 being the variable that showed greater association, with the CM followed of NO2 in the majority of provinces. Anyone of the health determinants considered, could explain the differential geographic behavior.

Conclusions
The role of PM10 is worth highlighting, as the chemical air pollutant for which there was a greater number of provinces in which it was associated with CM. The role of the meteorological variables—temperature and HA—was much less compared to that of the air pollutants. None of the social determinants we proposed could explain the heterogeneous geographical distribution identified in this study.

The European Union is currently immersed in policy development to address the effects of climate change around the world. Key plans and processes for facilitating adaptation to high temperatures and for reducing the adverse effects on health are among the most urgent measures. Therefore, it is necessary to understand those factors that influence adaptation. The aim of this study was to provide knowledge related to the social, climate and economic factors that are related to the evolution of minimum mortality temperatures (MMT) in Spain in the rural and urban contexts, during the 1983–2018 time period. For this purpose, local factors were studied regarding their relationship to levels of adaptation to heat. ; The authors gratefully acknowledge the grants for projects ENPY107/18; ENPY 376/18, ENPY 470/19 and ENPY 340/20 from the Carlos III Institute of Health, and is supported by the Biodiversity Foundation of the Ministry for Ecological Transition and Demographic Challenge.